A prefrontal motor circuit initiates persistent movement

Persistence reinforces continuous action, which benefits animals in many aspects. Diverse external or internal signals may trigger animals to start a persistent movement. However, it is unclear how the brain decides to persist with current actions by selecting specific information. Using single-unit extracellular recordings and opto-tagging in awake mice, we demonstrated that a group of dorsal mPFC (dmPFC) motor cortex projecting (MP) neurons initiate a persistent movement by selectively encoding contextual information rather than natural valence. Inactivation of dmPFC MP neurons impairs the initiation and reduces neuronal activity in the insular and motor cortex. After the persistent movement is initiated, the dmPFC MP neurons are not required to maintain it. Finally, a computational model suggests that a successive sensory stimulus acts as an input signal for the dmPFC MP neurons to initiate a persistent movement. These results reveal a neural initiation mechanism on the persistent movement.


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Persistence is a fundamental property that can help humans succeed in many areas, such as athletic and 22 social competition 1, 2 . It has a long-lasting effect on the maintenance of motivation 3 . It also contributes 23 to the animal's survival, such as dominating in social hierarchy and escaping from predators 4 . It can 24 drive actions successively from seconds to minutes and even outlast the eliciting stimulus. Socially 25 dominant animals exhibit effortful behaviors toward their rivals 5 . Repeated visual threat stimuli lead a 26 persistent state in Drosophila 6 . Although persistence is influential in both humans and animals, there 27 are few studies on how the brain applies it to movements. Moreover, the psychological definition of 28 persistence is not appropriate to solve certain behavioral problems, including how long it should last and 29 how to quantify its continuity, as it can vary from behavior to behavior. To examine persistence, we 30 focus on a motivated movement 7 that can persist for a certain period of time and stabilize at a range of 31 high frequency. This type of motivated movement is triggered by internal states from deep brain regions 32 and is regulated by cortex neurons 7,8,9 . It is also accompanied with extensive valence coding in the 33 brain 10, 11, 12, 13 . Here, we attempt to identify the neural coding in the different phases of motivated, 34 persistent movement and to test whether it is the same with the coding of valence. 35 Quantification of persistent movements 36 2 First, we defined a persistent movement as the continuous repetition of a single movement (e.g., a cycle 37 of tongue or limb movements) and the maintenance of that continuity for at least 5 seconds. To impose 38 a persistent movement on mice, they were deprived of water for 16 to 36 hours until their body weight 39 decreased by about 22% (Subject details, Methods). The mice were then head-fixed to a custom made 40 set-up and trained to lick various types of liquid after delivery onset (DO) (Fig. 1a, Supplementary Fig.  41 1b, and Behavioral details, Methods), but received no other artificial stimuli. Licking signals, facial and 42 locomotor activities were measured. After training, we observed that the mice showed licking 43 movements sustained for approximately 15-30 seconds during water delivery (Supplementary Fig. 1c-f). 44 Consistent with the standard pattern of affective dynamics performed previously 14 , licking frequency 45 was maximized during the initial phase (Supplementary Fig. 1c-f, right column, peaks of licking 46 frequency are indicated by black arrows) and then stabilized at 6 to 7 Hz until the end of delivery 47 (Supplementary Fig. 1d, e) or when the liquid was switched to quinine (5mM, Supplementary Fig. 1c, f). 48 Higher hedonic stimuli, 20% sucrose, did not increase this frequency (Supplementary Fig. 1e, f). As an 49 aversive stimulus, quinine administration was more likely than the interruption of water or sucrose 50 administration to cause termination of persistent licking (p<0.05 with respect to termination bias, 51 Supplementary Fig. 1i). In addition, we found no significant difference between male and female mice in 52 the initiation and termination of persistent licking movement (Supplementary Fig. 1r, s). Therefore, 53 quinine was used to terminate the persistent licking movement and to evaluate negative valence; by 54 contrast, water or sucrose was used to trigger licking movement and to evaluate positive valence. 55 Next, we determined the temporal window for the study of valence and movement phases. For valence, 56 the assessment of whether water or quinine is hedonic or aversive should be based on the single 57 contact (approximately 180ms around the licking onset (LO), Supplementary Fig. 1c-f) to them. For 58 movement phase, we divided it into an initial and a terminal phase. To our observation, the maximum 59 licking frequency was confined within the initial phase and a sharp decrease of licking frequency was in 60 the terminal phase ( Supplementary Fig. 1c-f), so we suspected that there was additional neural coding 61 to increase and decrease licking frequency besides the coding of licking command. Based on the period 62 of peak licking frequency (approximately 3s, as indicated by the arrow, Supplementary Fig. 1c) in the 63 initial phase and the sharp drop of licking frequency (approximately 3s from 6-7 Hz to less than 1 Hz, as 64 indicated by the arrow, Supplementary Fig. 1c) in the terminal phase, we designed a time window of 5-65 second long (including 2s baseline before the licking onset) as a temporal window for data analysis. 66 Together, we considered the neuronal firings of the valence and movement phases were confined 67 within a small (S, LO-100ms to LO+80ms) and a large (L, 1 st LO-2s to 1 st LO+3s) scale window, 68 respectively (Fig. 1c). 69 70 To characterize the single-units that statistically represent valence and movement phases, we used in 71

Dissociable neural coding of movement phases and valence
vivo silicon probe to collect neuronal activity data from all three brain regions, including the insular 72 cortex (IC), which is known to encode valence in the gustatory system 11, 15 , the primary motor cortex 73 (M1), which represents the motor commands of voluntary movement 16 , and the dorsal medial 74 prefrontal cortex (dmPFC), which has been shown to connect these two brain regions 17 . The single-units 75 that were generated from the neural activity data were then classified into different groups of neural 76 representations according to the extent to which they can discriminate liquid types (for valence) or 77 movement phases (Methods). 78 3 We then asked whether the neuronal coding of movement phases and valence are associable or not by 79 examining the proportion of neural representations of valence and movement phases in three brain 80 regions (IC, M1, and dmPFC). As population, we found that 27% of single-units represented movement 81 phases (initial or terminal phase, ≥65% in movement phase correlation) but showed weak or no taste 82 tuning ( Fig. 1d-f). 14% of single-units represented valence (positive or negative valence (PV or NV), 83 z>1.64 in taste correlation) but exhibited poor specification on movement phases (Fig. 1d-f). By contrast, 84 only small fraction (8%) of single units displayed the preference to both valence and movement phases 85 (Fig. 1e, f), though this number varied trivially from region to region (Supplementary Fig. 4). 86 To test whether the neural networks representing movement phases and valence interact with each 87 other, we examined the connectivity between the neurons solely tuned to initial phase and PV using 88 Total Spiking Probability Edges (TSPE) 18 and compared it with shuffled connectivity. Our results showed 89 that there is no overall excitatory impact from the initial phase to PV or from PV to initial phase tuned 90 neurons (mean of real TSPE < 99 percentile of shuffled TSPE, Supplementary Fig. 5d). Note that 91 connectivity between the terminal phase and NV tuned neurons was not available because their spike 92 data were unable to construct a cross-correlation in a 50ms-time window, suggesting that the coding of 93 terminal phase and NV are not connected. Together, these results suggest that the movement phases 94 and valence are encoded separately. 95 Coding of the initial phase in dmPFC MP neurons 96 Next, we investigated the neural basis representing the initial phase of persistent movement. We first 97 compared the fraction of clustered neural representations in the IC, M1, and dmPFC. Second, we 98 examined decoding performance in these brain regions by training a linear discriminant decoder on 99 firing rate data. Using cell-specific recordings enabled by the opto-tagging approach 19 ( Fig. 2a-c), we 100 found that motor cortex projecting (MP) neurons 17 in the dmPFC exhibited the best representation of 101 the initial phase among the three mapped brain regions (Fig. 2, Supplementary Fig. 4, 6, and 7). 102 Specifically, the neuronal clustering results showed that 32% of dmPFC MP neurons exhibited a degree 103 (≥65% shuffled activity at initial phase & ≤35% shuffled activity at terminal phase) of initial phase 104 representation (Fig. 2h), whereas only 3% of them exhibited a degree (≥65% shuffled activity at terminal 105 phase & ≤35% shuffled activity at initial phase) of terminal phase representation (Fig. 2h). No more than 106 20% of dmPFC MP neurons showed a degree (z>1.64 in valence correlations compared to shuffled 107 activity) of valence representation (PV+NV, Fig. 2e). The decoding performance results showed that 108 dmPFC MP neurons had a low ability to discriminate positive and negative valence (p<0.0001 lower than 109 shuffled cumulative decoding accuracy, Fig. 2k left), while had a high representation of movement 110 phases (p<0.0001 higher than IC and shuffled cumulative decoding accuracy, Fig. 2k right). To confirm 111 the discriminability of dmPFC MP neuron between valence and movement phases, we first embedded 112 neuronal population activity in the S-and L-window of dmPFC MP neurons into trajectories using 113 principal component analysis (PCA) and then measured the mean Euclidean distances in all PC 114 dimensions. The results showed that the discrimination of neural activity was significant higher in the L-115 window than it in the S-window (p<0.05, Fig. 2j), indicating that dmPFC MP neurons discriminate better 116 between movement phases than between valence values. 117 Bodily arousal is usually accompanied by valence changes 4 . It is, therefore, necessary to investigate 118 whether or not dmPFC MP neuron also encode bodily arousal. In rodents, facial activity can reflect 119 4 bodily arousal 20 . In the persistent licking task, the facial activity showed a reliable increase immediately 120 after water DO (p<0.05 compared to baseline in all sessions, Supplementary Fig. 1k-n). Thus, we used 121 facial activity to assess bodily arousal in the initial phase. To investigate whether the firing changes of 122 dmPFC MP neuron in the initial phase were due to bodily arousal, we trained a Hammerstein-Wiener 123 model and tested its prediction accuracy using facial activity data. Although some dmPFC neurons 124 exhibited a degree of facial activity representation in the initial phase (p<0.05 initial phase (median=-125 4.5671; 21/194 neurons with accuracy>30%) vs terminal phase (median=-36.9341; no neuron with 126 accuracy>30%), Supplementary Fig. 8d2), all dmPFC MP neurons showed poor prediction performance 127 at both initial (median=-14.5639; no neuron with accuracy>30%) and terminal phase (median=-27.4588; 128 no neuron with accuracy>30%) on facial activity and no significant difference between these two phases 129 (p>0.05, Fig. 2l, m) optogenetically manipulated by expressing stGtACR2 21 and shining laser during the different phases of 135 the persistent licking task (Fig. 3a). Our results showed that optogenetic silencing of dmPFC MP neurons 136 impaired the initiation of licking (p<0.001 compared to the sham trials, Fig. 3b, c), but had no effect on 137 the termination of lick (p>0.05 compared to the sham trials, Fig. 3d, e) or the licking frequency in the 138 middle phase (Supplementary Fig. 10b2) in thirsty mice, suggesting that dmPFC MP neurons are not 139 involved in motor control. The similarity of thirst level was confirmed by comparing body weight loss in 140 sham and laser trials (Supplementary Fig. 10b4, c3, and d3). To further confirm the function of dmPFC 141 MP neuron in initiating persistent licking movement, dmPFC MP neurons, expressing hm4D(Gi), were 142 chemogenetically silenced by administrating with CNO in mice and the licking chances were tested 143 during the persistent licking task. As expected, thirsty mice with chemogenetically silenced dmPFC MP 144 neuron had lower chance of drinking water (p<0.05 compared to the saline trials, Supplementary Fig.  145 9c). To investigate whether the dampened licking initiation was due to a decrease in bodily arousal, we 146 tested facial and locomotor activity with or without optogenetic silencing. The mice in the sham and 147 laser trials displayed the similar levels of facial and locomotor activity in all initial, middle, and terminal 148 phase ( Fig. 3g and Supplementary Fig. 10b-d), suggesting that the attenuated licking initiation was not 149 due to the decrease of bodily arousal. 150 We next asked whether the effect of dmPFC MP neuron silencing was specific to the initiation of 151 persistent licking or also general to other types of initiation of persistent behavior. We took advantage 152 that mice showed a phase of persistent running after a mild electric shock (Supplementary Fig. 9h). To 153 test whether inactivation of dmPFC MP neurons also affected this behavior, we examined body activity 154 after a 1s electrical tail shock. Indeed, chemogenetic silencing of dmPFC MP neurons suppressed 155 escaping behavior (Supplementary Fig. 9h, i), suggesting that dmPFC MP neurons are generally involved 156 in the initiation of persistent movements. This result is consistent with previously reported general 157 population of dmPFC neurons, silencing of which has been shown to delay the initiation of avoidance 158 movements 9 . Together with the evidence that dmPFC MP neurons are required for the persistent licking 159 movement, this also supports the theory of mixed selectivity in the prefrontal cortex 22 . 160 Next, we hypothesized that the increase in the brain state of positive valence and tongue movement in 161 the initial phase was due to the coding of movement phases. IC is thought to encode taste valence 11, 15 162 (38/42 in w-q vs 11/42 in w-s vs 33/42 in w-w with mean decoding accuracy higher than chance, 163 Supplementary Fig. 6a), whereas M1 was involved in voluntary control of tongue movement during 164 licking 23, 24 (12% neurons with z>1.64 in lick correlations compared with shuffled activity, 165 Supplementary Fig. 4b1). Therefore, the activities in IC and M1 should be affected by the optogenetic 166 silencing of dmPFC MP neurons. To test this hypothesis, we measured the neural activity in these two 167 brain regions with or without shining laser on dmPFC. As expected, the neuronal activities of M1 and IC 168 were decreased (p<0.05) after dmPFC MP neurons were optogenetically silenced (Fig. 4f, g). We further 169 confirmed this initial phase specificity by excluding the effect of silencing of dmPFC MP neuron on 170 valence at middle phase (no significant difference of sucrose-licking frequency between sham and laser 171 trials, Supplementary Fig. 10a) question, we built a neural network-based model (Fig. 5a) and examined how the output of licking 177 performance changes in response to different types of inputs. The design of this model was mainly 178 based on two criteria: (1) the inter-spike interval of a single neuron in the model is matched to the MP 179 neuron in mPFC (Supplementary Fig. 11a); (2) the neuronal population of modeled network performs 180 rotational dynamics 25 because we assumed that tongue movement follows a rhythmic pattern 181 (Supplementary Fig. 11b, c). To verify if the output of this model matched the performance of thirsty 182 mice in the actual experiment, we manipulated the firing rate of the simulated network by inserting the 183 inputs with different amplitudes and examined the output licking frequency and initiation bias. We 184 found no linear relationship between the mean neuronal firing rate and above two parameters 185 (Supplementary Fig. 11d

197
Our study identified a neural circuit, responsible for initiating persistent movements. After receiving a 198 sensory signal, dmPFC MP neurons can send the command signals to the primary motor cortex and 199 striatum, which in turn initiates the downstream machineries for a persistent movement (Fig. 6) that extracts only a few optimal aspects of stimulus while maintaining specificity. The question is why 220 some of the aspects are optimal but others are not. We suspect that these optimized aspects are the 221 ones that are most similar or closest in time to the reward or punishment itself, as they can efficiently 222 and directly link to a specific behavioral pattern. Second, how can the sensory cue be associated with a 223 particular movement pattern? Based on the reciprocal link between mPFC and motor cortex 17 , we 224 reason that a feedback loop between the generalization network and motor regions may be required. 225 Meanwhile, a valence input may be also necessary to construct the association and sensitize the 226 response to sensory cue. Transformation of neural representations. Some neurons may initially encode PV but abolish this 230 encoding property in subsequent phases. Other neurons may not encode PV in the initial phase but 231 adopt this encoding later. This possibility can explain the persistent valence encoding in the IC 232 (Supplementary Fig. 6a3); (2) Valence decreases. Since the thirsty mice may become satiated as the lick 233 progresses, the value of water to the mice will decrease accordingly. Similarly, the gradually increasing 234 activity of the neural representations of NV (Supplementary Fig. 4d, e) could also be caused by the 235 transformation of their encoding properties or by an increasing negative valuation of water. 236 The function of MP neuron in the IC-MP-motor circuit. Based on the structure of the circuit, the MP 237 neuron acts as a node connecting IC and motor region 17 . Since IC encodes valence, it is possible that the 238 MP neuron integrates valence signal and sends it to motor region. However, our result showed that MP 239 neuron does not encode valence (Fig. 2k). Because some of the neurons in IC also encode movement 240 phases ( Fig. 2k and Supplementary Fig. 4a), we speculate the function of this circuit is to transmit 241 movement phase signals, instead of valence, during persistent movement control. 242 Relationships to persistent spiking activity. Persistent spiking activity in prefrontal cortex has been 243 observed in both rodents and humans during the maintenance of working memory 28,29 . However, only a 244 few neurons in the prefrontal cortex showed persistent activity (small fraction of neural representations 245 of lick, Supplementary Fig. 4c, and gradually decreasing firing of neural representations of PV and initial 246 phase, Supplementary Fig. 4d were fixed to the stereotactic device (NARISHIGE SG -4N) and maintained at 37 o C with a heating pad 272 (K&H No. 1060). Seventy percent isopropyl alcohol and iodine were placed on the incision site. The skull 273 was exposed by cutting the skin and removing the dura and connective tissue. The coordinates used for 274 positioning the injection and implantation sites were relative to Bregma (antero-posterior A-P, medio-275 lateral M-L, dorsal-ventral D-V) in mm. After the surgeries, the mice were administered intraperitoneal 276 ibuprofen (50 mg kg -1 ), and they were kept at 37°C for 30-60 minutes before returning to the home 277 cage. 278 During the surgery, the skull was horizontally aligned through a fixing apparatus (Stoelting Co.). An 306 anchor screw was placed on the right cerebellum to connect ground wires of the electrodes. After 307 placing the anchor screw and electrodes, silicone sealant (kwik-cast, world precision instrument) was 308 applied above the exposed brain tissue. A customized head bar 309 (github.com/ywang2822/Multi_Lick_ports_behavioral_setup) was then positioned over the skull. To 310 affix the implant, Metabond (C&B Metabond, Parkell) and dental cement (Lang Dental) were applied. 311 The behavioral experiments started at least one week after the surgery. 312

Behavioral details 313
The head-fix setup was connected to a construction rod (Throlabs) by a 3d printed connector 314 (github.com/ywang2822/Multi_Lick_ports_behavioral_setup through the Bpod (Sanworks). The signal of mice locomotor activity was collected through an optical 321 shaft encoder (H5-360-IE-S, US digital). For facial videography set-up, the camera (S3-U3-91S6C-C, 322 Teledyne FLIR) was positioned at the right side of the mouse's lateral face surface, which illuminated by 323 two infrared arrays. For laser delivery, a solid-state laser (Shanghai Laser& Optics Century Co., 473 nm) 324 was connected to fiber optic patch cord (Doric Lenses), which attached to the implanted optic fibers 325 9 using ceramic mating sleeves. To conditionally control the laser delivery by water, sucrose, or quinine 326 onset, we used a 4-way data switch box (BNC, Kentek) to bridge the laser and solenoid valves. A 327 programmable stimulator (A-M system, model 4100) was used to control laser delivery and a voltage 328 pulse for tail shock experiment. All signals, including frame timing, wheel speed, liquid delivery timing, 329 lick timing, shock timing, and laser delivery timing, were sent to an USB interface board (Intan 330 Technologies, RHD). 331 For licking task, the deprivation was conducted until the body weight decreased approximately 22% (the 332 deprivation time was 16 to 36 hours, varied from mouse to mouse). During training phase, mice learned 333 to sense the water through their whiskers. We considered mice to become proficient at the task when 334 licking happened within 3s after the delivery onset (DO) in all repeated trials. During the test phase, we 335 first delivered water and 20% sucrose in a random sequence for a total of 30s. After at least 5 min, we 336 then orderly delivered water, 20% sucrose, and 5 mM quinine for 10s each or water and 5 mM quinine 337 for 15s each. 338 For tail shocking task, 16-23 volts electrical shocks were administered to the tail by a customized 339 shocker (electric shock box machine kit, STEREN). Two conductive adhesive copper tapes were 340 connected to the shocker and positioned 2 cm apart at the tail by sticking on customized heat shrink 341 tube (various on the circumference of mouse tail). During the first time of training, the voltage of 342 electrical shocks were adjusted until escaping behavior was observed (speed>10 cm s -1 right after the 343 shock). This voltage was recorded and used for the following tests. Those who did not perform escaping 344 behavior were excluded from the test. 345

Spike sorting and firing rate estimation 346
Before spike sorting, single unit data were acquired from 32-channel RHD head stage, which connected 347 with a signal acquisition system (USB board, Intan Technologies) with sampling rate at 20 kHz. All spike 348 sorting procedures were performed with an offline software Spikesorter 30, 31, 32 349 (swindale.ecc.ubc.ca/home-page/software/) under following parameters: (0.5 kHz and 4 poles high pass 350 Butterworth filter) for signal filtering, (noise calculation: median; threshold: 80µV, 5x noise, 0.75ms 351 window width) for even detection, and (pca dimensions = 2; template window: -0.8 to 0.8; starting 352 sigma = 5; threshold = 9) for clustering. We used Bayesian adaptive kernel smoother 33 with following 353 parameters, α = 4 and β = (number of spike events) ^ (4/5), to estimate the firing rate of sorted spikes. 354 For small scale temporal window (180ms), we used a bandwidth of 5ms. While for large scale temporal 355 window (5s), we used a bandwidth of 200ms. 356

Optogenetic silencing 357
We illuminated bilateral prefrontal cortices using 473 nm laser to activate stGtACR2 21 . Laser pulses 358 (40ms width at 20Hz) were delivered in a 5s duration. The onset of laser pulses was triggered based on 359 either water DO or quinine DO. The optogenetic silencing experiments were only performed after mice 360 reached stable behavioral level (after at least two test phases and (lick onset (LO) -DO < 3s) in all test 361 phases). The trials, of which licking frequency > 0.5 Hz in the time course 3s before DO, were excluded. 362 Histological characterizations were used to identify the viral infection. 363

Opto-tagging 364
We applied laser pulses (1ms width at 20 Hz, 3s duration) on the unilaterally prefrontal cortex of viral 365 (AAV-ChR2) injected mice. Laser and network-evoked spikes (see also Spike sorting and firing rate 366 estimation) were identified using the Stimulus Associated spike Latency Test (SALT 19 ). Specifically, laser 367 and network-evoked spikes were assessed in a 0-5ms and a 6-10ms temporal window after laser onset, 368 respectively. For those units with significant correlation (correlation coefficient > 0.85) of average 369 waveform and significantly different distribution (P < 0.05) of spike latency with baseline units were 370 identified as laser or network-evoked units. 371

Chemogenetic inhibition 372
Viral pAAV-hsyn-DIO-hm4D(Gi) (Addgene_44362-AAV5) injected mice were administered 373 intraperitoneally with Clozapine N-oxide dihydrochloride (CNO, 2mg kg -1 , Tocris) ten minutes before the 374 licking or tail shocking task. Only the mice reached stable behavioral level (after at least two test phases 375 and (LO -DO < 3s) in all test phases) were used for chemogenetic experiments. In the licking task, mice 376 were re-trained to lick the water one to two times after recovery from CNO administration. The re-377 trained phases were not included in test phases. 378

Analysis of facial and locomotor activity 379
We collected the frames during the licking or tail shocking task. We then converted these frames into 380 histogram of oriented gradients (HOG) vectors by using 8 orientations, 32 pixels per cell and 1 cell per 381 block. To extract the most variant facial part, we cropped the ear part with 364x296 pixels fixed size and 382 manually selected position of each transformed HOG vector 20 . Temporally adjacent HOG vectors were 383 paired, the facial activity at each time point was calculated as follows: 1-ΔR, where ΔR is the correlation 384 coefficient between two temporally adjacent HOG vectors. 385 The signals that collected from the encoder were digital pulses. The locomotor activity was calculated as 386 speed (cm s -1 ): • , where circumf is the circumference (cm) of the wheel, CPR (cycles per 387 revolution) is 360, and dt is the time interval between two digital pulses. 388

Analysis of licking initiation/termination bias 389
With the feeling of extremely thirsty, the mice will start a non-stop licking behavior when water is 390 available until feeling satiated or the delivery stopped 7 (Supplementary Fig. 1d). To evaluate if the mice 391 start or stop the continuous, but not discrete, lick, we calculated the initiation and termination bias. We 392 first calculated simple moving averages (SMAs) after water or quinine DO as following: was set in a range from 0 to 2 (nb-1); the number of poles was set in a range from 1 to 3 (nf); and the 492 degree of input nonlinearity estimator (one-dimensional polynomial) was set from 2 to 5. We then used 493 MATLAB function 'predict' to obtain decoding accuracies of facial dynamics from the test spike data. 494 Specifically, the data (four trials in total) were split 50/50. First two trials were used for training the 495 model and last two trials were used for testing. To avoid and overflow error, the values which lower 496 than negative 1500 were excluded. 497 Histology 498 To check the position of implanted electrodes and site of injection, mice were anesthetized with 2% 499 isoflurane (v/v) and perfused intracardially with 0.9% saline followed by 4% paraformaldehyde (PFA). 500 Fixed brains were washed three times before dehydration in 30% sucrose for 24 hr. Slices were cut on a 501 cryostat (MICROM, HM505E) at 70μm thickness after embedding with an optimal cutting temperature 502 compound (Tissue tek). Fluorescent images were acquired by an LSM 980 microscope (Zeiss), with a 10 × 503 0.45NA objective or a 2.5 × 0.085NA objective. 504

MP network-based model 505
The purpose of this modeling was to create lick raster readouts through giving a type of current input, 506 simulating a network of neural activities, and transforming the simulated spike timing to the lick timing. 507 where the excitatory and inhibitory synaptic time constants and were set as 5 and 10ms, 520 respectively. Other metrics were adjusted to adapt the inter-spike-intervals of L5 mPFC MP neuron, 521 which proposed to be a functional and dominate interneurons that bridge the gap between deep brain 522 regions and motor cortex 17 . The membrane potential was initiated randomly at a range from -65mV 523 to -63mV. The excitatory conductance was set at a range from 0 to 0.06ns and the inhibitory 524 counterpart was in 0 to 1.5ns. 525 The synapse action was dependent on the usage and availability of released neurotransmitter 526 before and after an action potential as following: = − • , = − • , where the 527 facilitation rate was set as 3.33 s -1 and the depression rate was set as 2 s -1 . The rest synaptic release 528 probability was set as 0.6. The probability of excitatory connection of the network was given as 0.1 and 529 the inhibitory counterpart was 0.2. 530 To generate lick raster data from the network, we first assumed that one single lick cycle is governed by 531 a rotational neural dynamic 25 . Then we divided one cycle of the lick into nine phases. The triggering 532 probability P of a lick signal was calculated from the network through a decision algorithm: 533 where S represents the standard deviation of the spike counts in whole phases. 535

Statistical test 536
For comparison of the mean of facial and locomotor activity (Supplementary Fig. 1k-n and  537 Supplementary Fig. 9e, h), comparison of the mean of licking frequency at different sessions 538 (Supplementary Fig. 1c-f), and comparison of TSPE (Supplementary Fig. 5), we used two-tailed Wilcoxon 539 signed rank test. For evaluation of the slope of spike counts trend line, we used one sample t test 540 (Supplementary Fig. 4e). The rest of statistical tests were two sample t test. 541

DATA AVAILIBILITY 542
The raw data that support these findings are available from corresponding author upon reasonable 543 request. Source data are provided with this paper. 544  showing channelrhodopsin-2 (ChR2) expression in MP neuron. c. Identification of labeled MP neuron. 576 We identified the unit as MP neuron when there was a significant probability of evoked spikes, 577 appeared from 0 to 5ms after laser onset (light blue), and when there was a high correlation (R>0.85) 578

CODE AVAILIBILITY
between evoked spikes (light blue waveform) and other spikes (black waveform    Fig 2. Histological and electrophysiological    The symbol + and -represent the significant positive and negative value of its labeled trend line slope, 685 respectively. + or -P<0.05, one sample t-test.  We thank Dr. Z. Zhang for imaging and C. Zhang for animal husbandry, histology assistance and items 872 purchasing. We thank O. Gonzalez for 3D printing and helps on behavioral set-up design. We thank Drs. set-up, collected and analyzed data, performed the simulation, and wrote the original manuscript. 879